{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f67275f2",
   "metadata": {},
   "source": [
    "Installing (updating) the following libraries for your Sagemaker\n",
    "instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb3acf74",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install .. # installing d2l\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ee705e",
   "metadata": {
    "origin_pos": 0
   },
   "source": [
    "# 全卷积网络\n",
    ":label:`sec_fcn`\n",
    "\n",
    "如 :numref:`sec_semantic_segmentation`中所介绍的那样，语义分割是对图像中的每个像素分类。\n",
    "*全卷积网络*（fully convolutional network，FCN）采用卷积神经网络实现了从图像像素到像素类别的变换 :cite:`Long.Shelhamer.Darrell.2015`。\n",
    "与我们之前在图像分类或目标检测部分介绍的卷积神经网络不同，全卷积网络将中间层特征图的高和宽变换回输入图像的尺寸：这是通过在 :numref:`sec_transposed_conv`中引入的*转置卷积*（transposed convolution）实现的。\n",
    "因此，输出的类别预测与输入图像在像素级别上具有一一对应关系：通道维的输出即该位置对应像素的类别预测。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ba53b71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:20.570706Z",
     "iopub.status.busy": "2023-08-18T07:07:20.570035Z",
     "iopub.status.idle": "2023-08-18T07:07:22.638674Z",
     "shell.execute_reply": "2023-08-18T07:07:22.637517Z"
    },
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b35251",
   "metadata": {
    "origin_pos": 4
   },
   "source": [
    "## 构造模型\n",
    "\n",
    "下面我们了解一下全卷积网络模型最基本的设计。\n",
    "如 :numref:`fig_fcn`所示，全卷积网络先使用卷积神经网络抽取图像特征，然后通过$1\\times 1$卷积层将通道数变换为类别个数，最后在 :numref:`sec_transposed_conv`中通过转置卷积层将特征图的高和宽变换为输入图像的尺寸。\n",
    "因此，模型输出与输入图像的高和宽相同，且最终输出通道包含了该空间位置像素的类别预测。\n",
    "\n",
    "![全卷积网络](../img/fcn.svg)\n",
    ":label:`fig_fcn`\n",
    "\n",
    "下面，我们[**使用在ImageNet数据集上预训练的ResNet-18模型来提取图像特征**]，并将该网络记为`pretrained_net`。\n",
    "ResNet-18模型的最后几层包括全局平均汇聚层和全连接层，然而全卷积网络中不需要它们。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37e86099",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-08-18T07:07:22.642480Z",
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     "shell.execute_reply": "2023-08-18T07:07:23.297190Z"
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    "origin_pos": 6,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /home/ci/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
     ]
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     "data": {
      "text/plain": [
       "[Sequential(\n",
       "   (0): BasicBlock(\n",
       "     (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (relu): ReLU(inplace=True)\n",
       "     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (downsample): Sequential(\n",
       "       (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "   )\n",
       "   (1): BasicBlock(\n",
       "     (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (relu): ReLU(inplace=True)\n",
       "     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       " ),\n",
       " AdaptiveAvgPool2d(output_size=(1, 1)),\n",
       " Linear(in_features=512, out_features=1000, bias=True)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_net = torchvision.models.resnet18(pretrained=True)\n",
    "list(pretrained_net.children())[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a6c6ca",
   "metadata": {
    "origin_pos": 8
   },
   "source": [
    "接下来，我们[**创建一个全卷积网络`net`**]。\n",
    "它复制了ResNet-18中大部分的预训练层，除了最后的全局平均汇聚层和最接近输出的全连接层。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92397bcf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:23.303038Z",
     "iopub.status.busy": "2023-08-18T07:07:23.302447Z",
     "iopub.status.idle": "2023-08-18T07:07:23.307017Z",
     "shell.execute_reply": "2023-08-18T07:07:23.306110Z"
    },
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "net = nn.Sequential(*list(pretrained_net.children())[:-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41361fe4",
   "metadata": {
    "origin_pos": 11
   },
   "source": [
    "给定高度为320和宽度为480的输入，`net`的前向传播将输入的高和宽减小至原来的$1/32$，即10和15。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6cbe7c99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:23.311746Z",
     "iopub.status.busy": "2023-08-18T07:07:23.310972Z",
     "iopub.status.idle": "2023-08-18T07:07:23.369499Z",
     "shell.execute_reply": "2023-08-18T07:07:23.368494Z"
    },
    "origin_pos": 13,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 512, 10, 15])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.rand(size=(1, 3, 320, 480))\n",
    "net(X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2aa79ff",
   "metadata": {
    "origin_pos": 15
   },
   "source": [
    "接下来[**使用$1\\times1$卷积层将输出通道数转换为Pascal VOC2012数据集的类数（21类）。**]\n",
    "最后需要(**将特征图的高度和宽度增加32倍**)，从而将其变回输入图像的高和宽。\n",
    "回想一下 :numref:`sec_padding`中卷积层输出形状的计算方法：\n",
    "由于$(320-64+16\\times2+32)/32=10$且$(480-64+16\\times2+32)/32=15$，我们构造一个步幅为$32$的转置卷积层，并将卷积核的高和宽设为$64$，填充为$16$。\n",
    "我们可以看到如果步幅为$s$，填充为$s/2$（假设$s/2$是整数）且卷积核的高和宽为$2s$，转置卷积核会将输入的高和宽分别放大$s$倍。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e32ef24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:23.374842Z",
     "iopub.status.busy": "2023-08-18T07:07:23.373922Z",
     "iopub.status.idle": "2023-08-18T07:07:23.405937Z",
     "shell.execute_reply": "2023-08-18T07:07:23.404771Z"
    },
    "origin_pos": 17,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "num_classes = 21\n",
    "net.add_module('final_conv', nn.Conv2d(512, num_classes, kernel_size=1))\n",
    "net.add_module('transpose_conv', nn.ConvTranspose2d(num_classes, num_classes,\n",
    "                                    kernel_size=64, padding=16, stride=32))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe867380",
   "metadata": {
    "origin_pos": 19
   },
   "source": [
    "## [**初始化转置卷积层**]\n",
    "\n",
    "在图像处理中，我们有时需要将图像放大，即*上采样*（upsampling）。\n",
    "*双线性插值*（bilinear interpolation）\n",
    "是常用的上采样方法之一，它也经常用于初始化转置卷积层。\n",
    "\n",
    "为了解释双线性插值，假设给定输入图像，我们想要计算上采样输出图像上的每个像素。\n",
    "\n",
    "1. 将输出图像的坐标$(x,y)$映射到输入图像的坐标$(x',y')$上。\n",
    "例如，根据输入与输出的尺寸之比来映射。\n",
    "请注意，映射后的$x′$和$y′$是实数。\n",
    "2. 在输入图像上找到离坐标$(x',y')$最近的4个像素。\n",
    "3. 输出图像在坐标$(x,y)$上的像素依据输入图像上这4个像素及其与$(x',y')$的相对距离来计算。\n",
    "\n",
    "双线性插值的上采样可以通过转置卷积层实现，内核由以下`bilinear_kernel`函数构造。\n",
    "限于篇幅，我们只给出`bilinear_kernel`函数的实现，不讨论算法的原理。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81e0e496",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:23.410931Z",
     "iopub.status.busy": "2023-08-18T07:07:23.410049Z",
     "iopub.status.idle": "2023-08-18T07:07:23.418870Z",
     "shell.execute_reply": "2023-08-18T07:07:23.417816Z"
    },
    "origin_pos": 21,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def bilinear_kernel(in_channels, out_channels, kernel_size):\n",
    "    factor = (kernel_size + 1) // 2\n",
    "    if kernel_size % 2 == 1:\n",
    "        center = factor - 1\n",
    "    else:\n",
    "        center = factor - 0.5\n",
    "    og = (torch.arange(kernel_size).reshape(-1, 1),\n",
    "          torch.arange(kernel_size).reshape(1, -1))\n",
    "    filt = (1 - torch.abs(og[0] - center) / factor) * \\\n",
    "           (1 - torch.abs(og[1] - center) / factor)\n",
    "    weight = torch.zeros((in_channels, out_channels,\n",
    "                          kernel_size, kernel_size))\n",
    "    weight[range(in_channels), range(out_channels), :, :] = filt\n",
    "    return weight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5b2c78",
   "metadata": {
    "origin_pos": 23
   },
   "source": [
    "让我们用[**双线性插值的上采样实验**]它由转置卷积层实现。\n",
    "我们构造一个将输入的高和宽放大2倍的转置卷积层，并将其卷积核用`bilinear_kernel`函数初始化。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c181ae97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:23.423829Z",
     "iopub.status.busy": "2023-08-18T07:07:23.422974Z",
     "iopub.status.idle": "2023-08-18T07:07:23.431177Z",
     "shell.execute_reply": "2023-08-18T07:07:23.430098Z"
    },
    "origin_pos": 25,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "conv_trans = nn.ConvTranspose2d(3, 3, kernel_size=4, padding=1, stride=2,\n",
    "                                bias=False)\n",
    "conv_trans.weight.data.copy_(bilinear_kernel(3, 3, 4));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75884a8b",
   "metadata": {
    "origin_pos": 27
   },
   "source": [
    "读取图像`X`，将上采样的结果记作`Y`。为了打印图像，我们需要调整通道维的位置。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cdbf1f0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:23.435665Z",
     "iopub.status.busy": "2023-08-18T07:07:23.435278Z",
     "iopub.status.idle": "2023-08-18T07:07:23.521627Z",
     "shell.execute_reply": "2023-08-18T07:07:23.520407Z"
    },
    "origin_pos": 29,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "img = torchvision.transforms.ToTensor()(d2l.Image.open('../img/catdog.jpg'))\n",
    "X = img.unsqueeze(0)\n",
    "Y = conv_trans(X)\n",
    "out_img = Y[0].permute(1, 2, 0).detach()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f8e306",
   "metadata": {
    "origin_pos": 31
   },
   "source": [
    "可以看到，转置卷积层将图像的高和宽分别放大了2倍。\n",
    "除了坐标刻度不同，双线性插值放大的图像和在 :numref:`sec_bbox`中打印出的原图看上去没什么两样。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bafc470",
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "input image shape: torch.Size([561, 728, 3])\n",
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      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2l.set_figsize()\n",
    "print('input image shape:', img.permute(1, 2, 0).shape)\n",
    "d2l.plt.imshow(img.permute(1, 2, 0));\n",
    "print('output image shape:', out_img.shape)\n",
    "d2l.plt.imshow(out_img);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28a121f",
   "metadata": {
    "origin_pos": 35
   },
   "source": [
    "全卷积网络[**用双线性插值的上采样初始化转置卷积层。对于$1\\times 1$卷积层，我们使用Xavier初始化参数。**]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3607f0c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:07:24.203681Z",
     "iopub.status.busy": "2023-08-18T07:07:24.203097Z",
     "iopub.status.idle": "2023-08-18T07:07:24.209142Z",
     "shell.execute_reply": "2023-08-18T07:07:24.208048Z"
    },
    "origin_pos": 37,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "W = bilinear_kernel(num_classes, num_classes, 64)\n",
    "net.transpose_conv.weight.data.copy_(W);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2a5afd",
   "metadata": {
    "origin_pos": 39
   },
   "source": [
    "## [**读取数据集**]\n",
    "\n",
    "我们用 :numref:`sec_semantic_segmentation`中介绍的语义分割读取数据集。\n",
    "指定随机裁剪的输出图像的形状为$320\\times 480$：高和宽都可以被$32$整除。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff06cc24",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-08-18T07:07:24.213186Z",
     "iopub.status.idle": "2023-08-18T07:07:55.535066Z",
     "shell.execute_reply": "2023-08-18T07:07:55.534048Z"
    },
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "batch_size, crop_size = 32, (320, 480)\n",
    "train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c83844",
   "metadata": {
    "origin_pos": 42
   },
   "source": [
    "## [**训练**]\n",
    "\n",
    "现在我们可以训练全卷积网络了。\n",
    "这里的损失函数和准确率计算与图像分类中的并没有本质上的不同，因为我们使用转置卷积层的通道来预测像素的类别，所以需要在损失计算中指定通道维。\n",
    "此外，模型基于每个像素的预测类别是否正确来计算准确率。\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "id": "244b4702",
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     "iopub.status.idle": "2023-08-18T07:08:45.398121Z",
     "shell.execute_reply": "2023-08-18T07:08:45.397216Z"
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    "origin_pos": 44,
    "tab": [
     "pytorch"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.443, train acc 0.863, test acc 0.848\n",
      "254.0 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
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   "source": [
    "def loss(inputs, targets):\n",
    "    return F.cross_entropy(inputs, targets, reduction='none').mean(1).mean(1)\n",
    "\n",
    "num_epochs, lr, wd, devices = 5, 0.001, 1e-3, d2l.try_all_gpus()\n",
    "trainer = torch.optim.SGD(net.parameters(), lr=lr, weight_decay=wd)\n",
    "d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)"
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  },
  {
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   "source": [
    "## [**预测**]\n",
    "\n",
    "在预测时，我们需要将输入图像在各个通道做标准化，并转成卷积神经网络所需要的四维输入格式。\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bdb803a3",
   "metadata": {
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     "iopub.status.idle": "2023-08-18T07:08:45.406358Z",
     "shell.execute_reply": "2023-08-18T07:08:45.405611Z"
    },
    "origin_pos": 48,
    "tab": [
     "pytorch"
    ]
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   "outputs": [],
   "source": [
    "def predict(img):\n",
    "    X = test_iter.dataset.normalize_image(img).unsqueeze(0)\n",
    "    pred = net(X.to(devices[0])).argmax(dim=1)\n",
    "    return pred.reshape(pred.shape[1], pred.shape[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d2aa8a",
   "metadata": {
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   "source": [
    "为了[**可视化预测的类别**]给每个像素，我们将预测类别映射回它们在数据集中的标注颜色。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "27e3aa15",
   "metadata": {
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     "iopub.status.idle": "2023-08-18T07:08:45.413358Z",
     "shell.execute_reply": "2023-08-18T07:08:45.412563Z"
    },
    "origin_pos": 52,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def label2image(pred):\n",
    "    colormap = torch.tensor(d2l.VOC_COLORMAP, device=devices[0])\n",
    "    X = pred.long()\n",
    "    return colormap[X, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a9d039",
   "metadata": {
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   "source": [
    "测试数据集中的图像大小和形状各异。\n",
    "由于模型使用了步幅为32的转置卷积层，因此当输入图像的高或宽无法被32整除时，转置卷积层输出的高或宽会与输入图像的尺寸有偏差。\n",
    "为了解决这个问题，我们可以在图像中截取多块高和宽为32的整数倍的矩形区域，并分别对这些区域中的像素做前向传播。\n",
    "请注意，这些区域的并集需要完整覆盖输入图像。\n",
    "当一个像素被多个区域所覆盖时，它在不同区域前向传播中转置卷积层输出的平均值可以作为`softmax`运算的输入，从而预测类别。\n",
    "\n",
    "为简单起见，我们只读取几张较大的测试图像，并从图像的左上角开始截取形状为$320\\times480$的区域用于预测。\n",
    "对于这些测试图像，我们逐一打印它们截取的区域，再打印预测结果，最后打印标注的类别。\n"
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   "execution_count": 15,
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     "iopub.status.idle": "2023-08-18T07:09:10.704851Z",
     "shell.execute_reply": "2023-08-18T07:09:10.704050Z"
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    "origin_pos": 56,
    "tab": [
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epRRCBKq6JstzRIiBV6pjNFqWDeuqRCiBs566ssg6EOwtHk9V1TRNg9Ey5mCEIATBYpLy+HTOfHbC16/eYZH8+l/8kjdvX7PdFjx98oTpZMTybsW2qKhWa65XKz54/hF3t7fcrVaUuxLb1iSJwRFIhgNEEws737x4Qd2s+PDRKWjLb//4gtXdGi8kSZoxyiSuLLEuGn+JIB1mDNuajRqSZVPSJMFXNaJtSLUhD5rv/+4fyZSMWWjpyQcj6nVFaRuyLKGoG3SqcQGMVAyTAUljkduSpqrQY+cwAoQINM4jgiBoBTpFyoB0LduioSwaAn0aXBAEOA/B2S4FHnE0VxolDVp5hPBoLalCQxABZST5UCMkIAc4lxECbMuG09kCheN8PuOjhw/w0jCdFkhl+PDpYxaznEGekmYZddNwfjZGCc369prNcovVGV9/rzg7XyAwrFc3tDd3LOYzVJqQqIBUcH31lqIu2Wy3aKW4ub0jCR4hNcLVtJVAOB8zxlJgyxIvhjRoirohm8P06Sk6wOWrF6zbhmqz5ubqHcPXC559+CGT6YhcCVpp8AgSkZDkGVLqLmkaMCbFBIsMAqxAP3EB5VsIHgt4oSi95qZuabxHaYmWguEgx9UNBA8xxsE7CCIQgkKJgNYDghBYHCYzTE1KQGBUxSQ1eJ3w8cef8vLVDwgZtW+93lDWa0bDjItprHK9Xr6jLjbMRyMq5/jDl79jcXLKcDik2G5xLmqfHkh++4//RLFaI0c5TVURQmAwnGG1pR4ojHQUN1dYk3G7XnFxfsJoNuPq6oYXr15TOo9UULWW6XAA3pEkksoGvFSY6QmtzFgsJrjKMRiPwFmEjF5T1TrUMGEqJ7z97itOz+ZkUmK0AJ8ihSHQJUFNgmujq50rgxPRJBij0X/wYJIET2BdWbRWZFKTpynKeqq6YjLPaduW5aokzw3bsgYU29WO8SRntd6hZfStB5MJOtEsry55/uCUu6LFOkmWOlKjuL65pK4a3r67xKgYc+jgefPqBYvsCdKuUc2OkRTUmyuCGePKHeu7lOWywrYtCKibgvX6jvp2x0ndcjfMcCrhcrVl6paMEw1jQ+0sRVNze7fk9dvXTOZT5osz6t2WTAUaJMF5mrrFdj59OsmQNlb7PArhLapqqTYt5Txlc7lCemiKHdbbCGMStHf89u//Xy7OFiACtgmYNMEHaK0lCIEg1j0CoI1GBoFQGl0GDVKTGmjLhtYGXOHIE03btPi6gGKLxiPZsdsGhPckiaZQFcYkCBkzkQFPXTqKXcC6lruq4HK5pqgFZ4sBQ9+yWZZUrWW5WUcj7T0K2JU17ZcveP5ozMlA49qAVCmLxQVWJSyLHcWmZL1ecbdZsd6uydOc3WaFNhC+X0GS4c0AUVRc3pYxmys3VLsCFWKMgVLgHNPJhOFgTKpvcdQk1uNtg8lzirolSEmqFVXVIhU4rfChpSm32LbENpI0TdCpoSkbyl3BYrFANg2r5ZLFxRmyi3SlF8zGU3ZlidTHdXwBQiGtRSsZjbOUJh4UIteUVEjZorXAlTtUKDkxno33GC1ITUOdwWazwgjLyUwxTBRCQulgtZMESibThOq2xbYea2LB/sHZYy6Xq5geCZ42CLyHt6s1Xzw/RZuMFkvbNnz9zZe8vd3x9eUK7wOJ7m0MnM0E54sJ00QhtaD2gqsSpokhkTFQDAg2xY40Sbgra+zdCq0kRVlSNxXJQGMbgURRVQ1F02CcRyoBJtYgEIZWKlItCE7SNA6CoG0aZFCkWuKznNZ6ppMJy3dXiNs12WhMYz1aK4qqIAgJOqaUAsRODqFAa/QqT8i7KKjK8lhwTwyDpo2SrxJaXxG8QgnRNQkEnBdo1eUmtKR2gqlyBC/QUpImgmBgnCasS9i2Fl8KsjRliudiNuVqtcTZvm0H5tOcZDDm3WrL3bbkfJyxKRuWuxIlQSlJCA4RFMEH1rstRsI4nyJcA0KjpUSEwDBJKGpL4zzLzZbGxgraYDRktyvZbVYkiaKyDVIp0jzF2bbr7tAI0VLVDYGAbz14xUi3vNvWlFWD0YFUa4pthfOe0XCEc/DizTsksL28JrlbYyW4tkWnKevNltEgJ0tSBsMRbdsgpEb6gH7yy19CVTFtPZPZGN94Glsx3FSkrqEp14Ta4UtwvkaJrvMiCFIV/XWEwAWNF4LMGAbpAMSGWkikEMzHOVXrESict5TbFaNBzqYqKEOL1po8UczGA15dr1hvC4pdSV01MafviR0VXaHe2+jV2ADr2hIuVxjpEUqhdUZRtVhrmYyH+KalbmNqwRiNSlLe3txgmyZ2e2hJCJbhSGN9wDeWyjdIHUu5LgiEUGQ6thkpLZFSUzcWoxO2RYNUgrytadqKPEnRRkAmWd2ucSomDH1r0VJi65bGebCWum27LLZH/8vpjEoXDAc5Js1jlGxrPv7VJ+hE8/vf/gNvvv0nvDbYaotnEzORQmJcoKVFSUPderaNRo8kWjcMxwlJUKTJjHyQUZYV26JCSRBSMZ6PuFrfkRrXNSdovIPl3Za6sXgP1ovuQaOXRvCA7yRWIIMgQeOdxIXYcRT74wz5aIIZ5Hh7hyNEgjvP6zdvIbiYgm8teaZxbYyXvG/Riph2UIFEaTa7CqRkmI4RIpY6m7YBDK2NcYaWAoVnnCtKH0iyIaJtGY0Dt6sN6TAjMYqBSahtgw+BxsdGNyEkCIH+5PmnSKUYj0cgJXfLW9qm5YMPnqGShCwfMMoSrt++YHd3hb1pkTiQBi0CQyHQWUqoKoSCdeVxQjMYzZmN5kg15tn0gtVqxd3yGucs2qTkg4zNdsXtzQ2tjaBZ101MfQiF1oLxQCFVyuauQfjYkZGahCAE3rmYMbaWNBugpSJ0LiJSkGTJvujjg8coHZNybUOiNblJqHxD7SzegbASow35YEhR1zRNixWe1jlEiJlWbxx1U1O1LWcnCwZpgm0bgvBU1qEbzfhkwZ//xb9GS8nf/sf/gxOTEZwjTQ1NHZmaGB2dFBkwUuG8Qz/98COUPHTmTSYz2taS5xkBwfn5I9qPdtimIksVhJbNak2SDslEiNIVGlyiUSYa+jwdc7Z4ysMPPsOYISen5wQ8b1+/ZjyZMBgM+frrL7ld3nKzvAEBqouuTSLZ7sqYM6qgdi1CSYIPhI4ZBAgKRsMBi3RECHB6fsEv/sWvWK1XvH7ziizLKKuSq9sluqu5RLvY1+cFShicd1jv0T6QSNU9R6yTt1hQsbaMUhgVS8RCarbrFSHPaJoWLwUnJ3OSJGdx+hClEwgReuq6INEKZTISEnSSYkzCbrNBK4m1LUKA7lUjdikIkkSRplnMaRPYrjasVpcxsSYgSVNGsxOCMBgl0aLF4sjRrNdLRsMhk+mM+eljRqM5T599zPzkFGU0H3/yc6QQ3N7cxIJQcNzcXLHbFeRpirWePB8xGE6om4Z8mHPz5nuCC7S2RUmN69o0B8mAf/Pf/w/88vNfcnl9zfn5BZPZnLvlksXZOS9ffs+m2CK1YpDlXRuPxwcbjb4VKKU5W5zx6u0bvPBkecpnn31G+fddAwKgVOwiRCpcUEgBMkT3s7UuZqEBoVN+9Zf/FQ8uLgjO8tXvfwO2ifGVc7RVRWoS2iCYzOYUmy0ueKqupqGN0V07jABx6AVFCAIe72NyTUlDEwyTySmtbahqB8FilEKEgGtrhkYyytOus08xnc45PT0jSVOEEGQmIRBYnCxi55+EzWZLWRYYY3jx8gVSGKaTMUmScLfd8PrqLbgalaSgZGwOlpG46/WW8ckps9NzxuMxl5eXmCTl088/Z3F2yl//9f+GtxatFEmW4b1lV8XkotQSF2IxX0tJoiSPHj7GdMUcpRTGGFJjKMod292O+cjExjDvu8ZpRRCBLDF88OQDPnr+MbatuF0tub6+QicpZVmitcb7QJoPaHclu7u7WH4NAaU0xih0YnT0aTviCwH7qh2S+XzO2fkDjDHcXSlwNUYbVpsNVb2jrWKE6pqWNMtJ0xSdnaBNSpJotJZoJYl2VCCRmPGQfJAym454+PARQsCrVy+ZTGe8evmSLMuYTiakeRpdxN0Oo5OuEiJQdN2CxjCdTPZdFefn5xS7gqIs+ODZc8aTCc5a2q68KQloqWI0ryFPhzy4eMBmt0YpQZIktHVF8A6tZIQtK8jTHCUCiVIE67suQhXfRkpm0xlNU1OWBcvbK37zj3/PeDjm6uodaZbT1DVCOIqqIR+OybSiaSvquiVLFVmeo3Vs346FFCEQRGYQICAYjaacXzzCW8tkPOP8wSN8sHzz5e9pyoLt6prt7RsGSYKUniwbEZQhz7NYTmwahoPBofuva1NIjWaU5ywWpzRNw2az5mS+YDQasV6tePP6JV9++QdwllE+wDrfNbMJTJLwxRdf8O2333B9fQlI8iwjTWMv0s31FT/88B2Xl+/iOIBUIBXW1fgQe1+DCJjEAILJeIpzDS9e/kCmk729NNqgtebTTz7lh6//SJoItFUoEatxQgrKokGZlMXJCfP5FKMlv/r1X/Lqh+9pW4dOdexKcR7nHEk6IHiLkIrBINqo8XgWWy6j7ItoFQQxJ9K1TyoU5w+ekA1ypFDMZicIIThZXCDwXL59xbdf/QbhHImSbKsdQSSkJmE2nXJ6srg3x9B36/WfSgZCiB3cdV1xe3tL28a+J60lzz54xs3yls2upChLXPAorRkOhlxf3XJ9fcWzD55HSA1wffmOqijYrlas7lY0TYuQgsXJCTdXb9FK4z0kKuHRxSOC9QzzIVUVcz7OuZh7C4HBaMhoPObJkye8ffktg1QxCJLNtkaqWCDTWmN0bPP505/+hBaCb7/9E99+8xWD8ZDFbM7LF99iReD0/ALnBdaCFKqzWzEPpUXHAN81IPfFUDrpFUJgZMLi9AFSSCQR2xeLBQLBbH7K0w+e46yjaVrqasdydctoNOXi4lHsTX2v4/z9TauExeIUpRR1XeGsJ89GnHx0CkLw8vUb6rZFGcU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      "text/plain": [
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     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')\n",
    "test_images, test_labels = d2l.read_voc_images(voc_dir, False)\n",
    "n, imgs = 4, []\n",
    "for i in range(n):\n",
    "    crop_rect = (0, 0, 320, 480)\n",
    "    X = torchvision.transforms.functional.crop(test_images[i], *crop_rect)\n",
    "    pred = label2image(predict(X))\n",
    "    imgs += [X.permute(1,2,0), pred.cpu(),\n",
    "             torchvision.transforms.functional.crop(\n",
    "                 test_labels[i], *crop_rect).permute(1,2,0)]\n",
    "d2l.show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n, scale=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82349b4",
   "metadata": {
    "origin_pos": 58
   },
   "source": [
    "## 小结\n",
    "\n",
    "* 全卷积网络先使用卷积神经网络抽取图像特征，然后通过$1\\times 1$卷积层将通道数变换为类别个数，最后通过转置卷积层将特征图的高和宽变换为输入图像的尺寸。\n",
    "* 在全卷积网络中，我们可以将转置卷积层初始化为双线性插值的上采样。\n",
    "\n",
    "## 练习\n",
    "\n",
    "1. 如果将转置卷积层改用Xavier随机初始化，结果有什么变化？\n",
    "1. 调节超参数，能进一步提升模型的精度吗？\n",
    "1. 预测测试图像中所有像素的类别。\n",
    "1. 最初的全卷积网络的论文中 :cite:`Long.Shelhamer.Darrell.2015`还使用了某些卷积神经网络中间层的输出。试着实现这个想法。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "314d9c7f",
   "metadata": {
    "origin_pos": 60,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/3297)\n"
   ]
  }
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